Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty

Radio frequency identification (RFID) is widely used for item identification and tracking. Due to the limited communication range between readers and tags, how to configure a RFID system in a large area is important but challenging. To configure a RFID system, most existing results are based on cost minimization through using 0/1 identification model. In practice, the system is interfered by environment and probabilistic model would be more reliable. To make sure the quality of the system, more objectives, such as interference and coverage, should be considered in addition to cost. In this paper, we propose a probabilistic-based multi-objective optimization model to address these challenges. The objectives to be optimized include number of readers, interference level and coverage of tags. A decomposition-based firefly algorithm is designed to solve this multi-objective optimization problem. Virtual force is integrated into random walk to guide readers moving in order to enhance exploitation. Numerical simulations are introduced to demonstrate and validate our proposed method. Comparing with existing methods, such as Non-dominated Sorting Genetic Algorithm-II and Multi-objective Particle Swarm Optimization approaches, our proposed method can achieve better performance in terms of quality metric and generational distance under the same computational environment. However, the spacing metric of the proposed method is slightly inferior to those compared methods.     

A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems

This work presents a new approach for interval-based uncertainty analysis. The proposed approach integrates a local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective genetic algorithm. Anti-optimization is a term for an approach to safety factors in engineering structures which is described as pessimistic and searching for least favorable responses, in combination with optimization techniques but in contrast to probabilistic approaches. The algorithm is applied and evaluated to be efficient and effective in producing good results via target matching problems: a simulated topology and shape optimization problem where a ‘target’ geometry set is predefined as the Pareto optimal solution and a constrained multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set.     

A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem

In recent years, the historical data during the search process of evolutionary algorithms has received increasing attention from many researchers, and some hybrid evolutionary algorithms with machine-learning have been proposed. However, the majority of the literature is centered on continuous problems with a single optimization objective. There are still a lot of problems to be handled for multi-objective combinatorial optimization problems. Therefore, this paper proposes a machine-learning based multi-objective memetic algorithm (ML-MOMA) for the discrete permutation flowshop scheduling problem. There are two main features in the proposed ML-MOMA. First, each solution is assigned with an individual archive to store the non-dominated solutions found by it and based on these individual archives a new population update method is presented. Second, an adaptive multi-objective local search is developed, in which the analysis of historical data accumulated during the search process is used to adaptively determine which non-dominated solutions should be selected for local search and how the local search should be applied. Computational results based on benchmark problems show that the cooperation of the above two features can help to achieve a balance between evolutionary global search and local search. In addition, many of the best known Pareto fronts for these benchmark problems in the literature can be improved by the proposed ML-MOMA.     

物流配送路径多目标优化的聚类-改进遗传算法

探讨运输车辆路线安排调度问题的解决方法，提出一种先用优先级综合聚类分析法将客户分类，再用带有控制开关系统的改进遗传算法求解多目标VRP的优化方法。构造了一种随机开关，以此控制遗传算法中的变异运算，增加了群体的多样性，避免了遗传算法中“局部最优现象”的发生。计算机仿真实验证明了该算法的有效性。     

Genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line

Re-entrant production lines, such as those which occur in micro-electronic wafer fabrication, are characterized by a product routing that consists of multiple visits to a facility during the manufacturing process. With the development of micro-electronic technology, the research on the scheduling and control problem of re-entrant micro-electronic production line has attracted more and more people from both academia and industry to study and has become a challenging research subject. Some results of the scheduling of re-entrant micro-electronic production line based on heuristic sequence rules have been obtained. However, performances of these sequence rules are not good enough in re-entrant micro-electronic production line because of their sensitivity to the variation of types of production line. A genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line is proposed in this paper. Comparisons between the proposed algorithm and some other typical sequence rules have been made through the simulations of a practical micro-electronic production line. The static and dynamic simulation results show that the algorithm has considerable improvements on performances of the micro-electronic production such as mean cycle time, mean number of work-in-process, production rate.     

Distance-based and stochastic uncertainty analysis for multi-criteria decision analysis in Excel using Visual Basic for Applications

A program has been developed in Excel and written in Visual Basic for Applications, which enables a decision maker to examine the robustness of a solution obtained when using multi-criteria decision analysis (MCDA). The distance-based and stochastic uncertainty analysis approaches contained in the program allow a decision to be made with confidence that the alternative chosen is the best performing alternative under the range of probable circumstances. The uncertainty analysis methodology overcomes the limitations of existing sensitivity analysis techniques for MCDA by enabling all of the input parameters to be varied simultaneously within their expected ranges. The Weighted Sum Method (WSM) and PROMETHEE are the MCDA techniques available for the user to select in the program. The program is illustrated by applying it to a sustainable water resource development problem in the Northern Adelaide Plains, South Australia.     

Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

The main difficulty with EM algorithm for mixture model concerns the number of components, say g. This is the question of model selection, and the EM algorithm itself could not estimate g. On the contrary, the algorithm requires g to be specified before the remaining parameters can be estimated. To solve this problem, a new algorithm, which is called stepwise split-and-merge EM (SSMEM) algorithm, is proposed. The SSMEM algorithm alternately splits and merges components, estimating g and other parameters of components simultaneously. Also, two novel criteria are introduced to efficiently select the components for split or merge. Experimental results on simulated and real data demonstrate the effectivity of the proposed algorithm.     

An improved algorithm for hierarchical clustering using strong components

In 1982 the author presented an O(m(log n)²) time algorithm for hierarchically decomposing a directed n-vertex, m-edge graph with weighted edges into strong components. Such an algorithm is useful in cluster analysis of data with an asymmetric similarity measure. The present paper gives a simpler algorithm with the faster running time of O(m log n).     

Arm motion analysis using genetic algorithm for rehabilitation and healthcare

The worlds population is quickly aging. With an aging society, an increase in patients with brain damage is predicted. In rehabilitation, the analysis of arm motion is vital as various day to day activities relate to arm movements. The therapeutic approach and evaluation method are generally selected by therapists based on his/her experience, which can be an issue for quantitative evaluation in any specific movement task. In this paper, we develop a measurement system for arm motion analysis using a 3D image sensor. The method of upper body posture estimation based on a steady-state genetic algorithm (SSGA) is proposed. A continuous model of generation for an adaptive search in dynamical environment using an adaptive penalty function and island model is applied. Experimental results indicate promising results as compared with the literature.     

Spatial analysis for functional region of suburban-rural area using micro genetic algorithm with variable population size

A micro genetic algorithm with variable population size (VPμGA) is proposed for the spatial analysis for the functional region of suburan-rural area, in which, the fitness function is implied by a functional region affecting index (Θ) with a ‘law-of-gravity’ interpretation. The VPμGA evaluates the Θ represented dynamical behaviours over a ‘short’ to ‘long’ term period, which also revisits the urbanisation of Beijing and examines the Θ sensitivity to the functional distance of 13 suburban-rural districts. Numerical results with given statistics has been obtained using a specially devised simulation toolkit, it is shown that the VPμGA method can work valuably as a tool for providing a functional distanced based estimation of the inter-relationships between the enterprises number, the regional profit, the local population, the regional employment, etc., and to use this understanding to evaluate suburban-rural districts that are more resilient and adaptable.